{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据  训练集和测试集t都出现的活动"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   c_1  c_2  c_3  c_4  c_5  c_6  c_7  c_8  c_9  c_10   ...     c_92  c_93  \\\n",
      "0    2    0    2    0    0    0    0    0    0     0   ...        0     1   \n",
      "1    2    0    2    0    0    0    0    0    0     0   ...        0     0   \n",
      "2    0    0    0    0    0    0    0    0    0     0   ...        0     0   \n",
      "3    1    0    2    1    0    0    0    0    0     0   ...        0     0   \n",
      "4    1    1    0    0    0    0    0    2    0     0   ...        0     0   \n",
      "\n",
      "   c_94  c_95  c_96  c_97  c_98  c_99  c_100  c_other  \n",
      "0     0     0     0     0     0     0      0        9  \n",
      "1     0     0     0     0     0     0      0        7  \n",
      "2     0     0     0     0     0     0      0       12  \n",
      "3     0     0     0     0     0     0      0        8  \n",
      "4     0     0     0     0     0     0      0        9  \n",
      "\n",
      "[5 rows x 101 columns]\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"final_event.csv\")\n",
    "#event_data = train_data\n",
    "event_data = train_data.iloc[:,10:]\n",
    "print(event_data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dk6R5Z0HtUVQV+54/9IIT2Ye9fMVSnvzWAb51YGwOeiZJ89eCCopnuy/bTRUU6849k+Gh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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21dcdaf53c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "import seaborn as sns\n",
    "sns.distplot(event_data[\"c_other\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化处理。其实这一步做不做没啥区别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00091491,  0.        ,  0.02352941, ...,  0.        ,\n",
       "         0.        ,  0.00093129],\n",
       "       [ 0.00091491,  0.        ,  0.02352941, ...,  0.        ,\n",
       "         0.        ,  0.00072434],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.00124172],\n",
       "       ..., \n",
       "       [ 0.        ,  0.01219512,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.00103477],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.00062086],\n",
       "       [ 0.00274474,  0.01219512,  0.02352941, ...,  0.        ,\n",
       "         0.        ,  0.01365894]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = MinMaxScaler()\n",
    "std.fit_transform(event_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进行主成分分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13418, 3)\n",
      "[[  1.41294175e-01   8.51998198e-03   7.36893703e-03   5.42698653e-03\n",
      "    1.12113886e-01   1.90966692e-02   1.28557012e-01   2.40208019e-03\n",
      "    4.38195784e-03   2.16421566e-03   2.25776456e-03   1.99206803e-03\n",
      "    2.65371296e-03   2.82870811e-03   1.56065164e-03   2.69699027e-03\n",
      "    1.82041283e-03   3.15285819e-02   1.57364901e-03   1.43670546e-03\n",
      "    2.79559986e-03   3.13107915e-02   1.49960344e-03   5.55348873e-04\n",
      "    1.19622939e-03   8.83942644e-04   1.28976964e-03   1.40127718e-03\n",
      "    5.38662704e-04   1.11350547e-03   1.75095430e-03   7.73326888e-04\n",
      "    8.46450861e-04   6.84202125e-04   7.11575841e-04   1.61680681e-03\n",
      "    7.87372729e-04   4.58720981e-04   6.36794782e-04   1.35698028e-03\n",
      "    8.56913258e-04   5.93228371e-04   1.00997902e-03   1.51995064e-03\n",
      "    1.28457318e-03   7.39221389e-04   5.43515142e-04   7.94298259e-04\n",
      "    7.90499207e-04   3.84515644e-04   9.04635265e-04   2.05648069e-02\n",
      "    6.47503516e-04   7.87613576e-04   8.16808095e-04   5.46146762e-04\n",
      "    4.10294725e-04   5.09249234e-04   2.82848768e-04   8.79441304e-04\n",
      "    3.15965123e-04   5.07205349e-04   1.06959246e-03   9.62151672e-04\n",
      "    4.41111008e-04   2.06782735e-05   3.82407770e-04   4.60706388e-04\n",
      "    4.90178774e-04   2.27281692e-04   2.38684854e-04   4.44255622e-04\n",
      "    6.14070394e-04   3.10964170e-04   2.41530419e-04   3.03897852e-04\n",
      "    4.97074978e-04   8.45822467e-04   1.46844582e-03   5.36842177e-04\n",
      "   -1.48822769e-05   3.55079070e-04   2.61228808e-04   2.53284916e-04\n",
      "    4.29217570e-04   6.52402825e-04   2.12922793e-04   6.91472302e-04\n",
      "    4.21244504e-04   4.71400529e-04   4.97239550e-04   3.01639576e-04\n",
      "    4.02044524e-04   4.19802089e-04   3.20915544e-04   1.02993325e-03\n",
      "    1.06213368e-01   2.29238165e-04   3.79629089e-04   3.17458927e-04\n",
      "    9.67789167e-01]\n",
      " [  4.85364454e-01  -4.39090616e-02  -3.86326299e-02  -2.91742508e-02\n",
      "    4.20823738e-01  -1.42494251e-01   5.12861827e-01  -1.25716193e-02\n",
      "   -2.36572887e-02  -1.15619184e-02  -1.15584372e-02  -9.53424340e-03\n",
      "   -1.44520590e-02  -1.47734328e-02  -8.34875904e-03  -1.41250433e-02\n",
      "   -9.48235758e-03   1.21222283e-01  -8.56713936e-03  -6.89250211e-03\n",
      "   -1.85627223e-02   1.21461374e-01  -8.90247370e-03  -2.77852132e-03\n",
      "   -5.56742588e-03  -4.49568383e-03  -7.15300987e-03  -7.68050666e-03\n",
      "   -2.48886658e-03  -6.66229925e-03  -9.16494311e-03  -4.31590995e-03\n",
      "   -4.75078222e-03  -4.25484057e-03  -3.83514986e-03  -6.63071386e-03\n",
      "   -4.18383809e-03  -3.95074569e-03  -2.65754904e-03  -7.74987696e-03\n",
      "   -3.65039381e-03  -3.06433572e-03  -5.83744401e-03  -9.29688060e-03\n",
      "   -7.24272110e-03  -2.72158845e-03  -2.12875232e-03  -5.25788624e-03\n",
      "   -4.23855792e-03  -1.92083405e-03  -5.10456650e-03   6.66770947e-02\n",
      "   -3.72315776e-03  -4.13060093e-03  -4.65317676e-03  -3.34745916e-03\n",
      "   -2.11496176e-03  -2.74683740e-03  -1.41104379e-03  -4.64342660e-03\n",
      "   -1.72849455e-03  -2.90309989e-03  -6.91845546e-03  -5.14440960e-03\n",
      "   -2.33708463e-03  -6.70986300e-05  -1.95492293e-03  -2.38032410e-03\n",
      "   -2.62724553e-03  -1.12160700e-03  -1.95639712e-03  -2.71258508e-03\n",
      "   -3.23240489e-03  -1.87835388e-03  -1.26552380e-03  -1.77534788e-03\n",
      "   -2.74635144e-03  -2.81846847e-03  -1.04849122e-02  -1.69125989e-03\n",
      "    3.24841437e-04  -2.12964773e-03  -1.57661558e-03  -1.16432208e-03\n",
      "   -2.72031403e-03  -3.89831630e-03  -1.09498761e-03  -1.84017605e-03\n",
      "   -2.41863183e-03  -2.48029053e-03  -2.55663159e-03  -1.63246394e-03\n",
      "   -2.04845706e-03  -2.22245421e-03  -1.77851841e-03  -5.55757116e-03\n",
      "    4.51094793e-01  -1.19306350e-03  -2.46918203e-03  -1.75392008e-03\n",
      "   -2.42190855e-01]\n",
      " [  1.35447571e-02  -1.22258199e-01  -9.47094507e-02  -7.71277378e-02\n",
      "    2.90517552e-02   9.66195809e-01   9.38009920e-02  -3.17074193e-02\n",
      "   -5.35570867e-02  -7.97387206e-03  -2.62746912e-02  -2.23110430e-02\n",
      "   -2.42254790e-02  -4.07078266e-02  -1.75964471e-02  -3.09720749e-02\n",
      "   -2.21808009e-02   2.96550723e-03  -8.84611622e-03  -1.84195306e-02\n",
      "    2.04359696e-02   6.25110733e-03  -5.64464786e-03  -7.85065382e-03\n",
      "   -1.43753942e-02  -1.43825715e-02  -8.86878453e-03  -1.41279719e-02\n",
      "   -1.16102372e-02   3.46651810e-03  -2.41648881e-02  -1.05691118e-02\n",
      "   -2.52689532e-03   1.17197722e-03  -7.39121584e-03  -2.01420586e-02\n",
      "   -1.02669015e-02  -5.31597489e-03  -6.49812331e-03   4.48226559e-03\n",
      "    3.89632050e-03  -9.28987404e-03  -6.56574282e-03  -1.68094419e-03\n",
      "    1.96380239e-02  -9.11043265e-03  -8.83824557e-03   1.77517140e-02\n",
      "   -8.77254700e-03  -7.29342787e-03  -6.43428324e-03   4.72673432e-02\n",
      "   -1.18417792e-03  -1.15629471e-02  -5.11006876e-03  -3.88281209e-03\n",
      "   -4.96045938e-03   4.29241958e-03  -3.96792813e-03  -9.29412532e-03\n",
      "   -2.88779531e-03   9.01111484e-03   8.94942569e-05  -9.06497786e-03\n",
      "   -2.93903244e-03   5.43997903e-04  -6.55868836e-03  -6.40830364e-03\n",
      "   -6.80043339e-03  -4.48715024e-03  -1.76619391e-03   4.87119937e-05\n",
      "   -8.17329681e-03  -1.11126769e-03  -2.96326998e-03  -2.68834038e-04\n",
      "   -5.36510729e-03  -7.29627029e-03   8.96773191e-03  -6.49614595e-03\n",
      "   -1.59267366e-03   1.17798100e-03   8.10882319e-05  -6.10553454e-03\n",
      "    1.35131911e-03   2.51495274e-04  -2.90421898e-03  -8.45806340e-03\n",
      "   -2.07280204e-04  -5.87082113e-03  -7.70690124e-03  -4.04781267e-03\n",
      "   -5.97939398e-03  -5.82873606e-03  -3.19755995e-03  -1.31572721e-02\n",
      "    8.73400154e-02  -2.93340917e-03   3.53928691e-03  -9.96614607e-04\n",
      "   -4.44932492e-02]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "pca.fit(event_data)\n",
    "\n",
    "event_pca_data = pca.transform(event_data)\n",
    "\n",
    "# 降维后的特征维数\n",
    "print(event_pca_data.shape)\n",
    "print(pca.components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 显示PCA的Explained Variance图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pca_results(event_data, pca):\n",
    "    dimensions =  ['Dimension {}'.format(i) for i in range(1,len(pca.components_)+1)]\n",
    "\n",
    "    # PCA components个数\n",
    "    components = pd.DataFrame(np.round(pca.components_, 5), columns = event_data.keys())\n",
    "    components.index = dimensions\n",
    "\n",
    "    # 创建bar plot图\n",
    "    fig, ax = plt.subplots(figsize = (14,10))\n",
    "\n",
    "    components.plot(ax = ax, kind = 'bar');\n",
    "    ax.set_ylabel(\"Feature Weights\")\n",
    "    ax.set_xticklabels(dimensions, rotation=0)\n",
    "\n",
    "    # 显示explained variance ratios\n",
    "    for i, ev in enumerate(pca.explained_variance_ratio_):\n",
    "        ax.text(i-0.3, ax.get_ylim()[1], \"Explained Variance\\n%.4f\"%(ev))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看出第一主成分的explained variance已经达到0.959. c_other(除了最常用的100个词之外的其余词出现的次数)起到主导作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XKxAIaMKEwSXIvffeq3nz5qm6ulpTpkzRvHnzdM8995Qjdk5YYQIAAACQt3g8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\nAAAAEAIDEwAAAACEwCl5AAAAwA3EcqgxK5LP83720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OPGjT6LxRL0+XymkbZMqampg7W1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\nEwAAAACEQcEEAAAAAGH8f5H7pQZ4r/L2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21dcff424e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_result_pic = pca_results(event_data, pca)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进行聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans\n",
    "import time\n",
    "from sklearn import metrics\n",
    "def cluster_processing(k,event):\n",
    "    \n",
    "    print(\"K-means begin with clusters: {}\".format(K));\n",
    "    start = time.time()\n",
    "    \n",
    "    mb_kmeans = MiniBatchKMeans(n_clusters = K,random_state=0)\n",
    "    mb_kmeans.fit(event)\n",
    "    \n",
    "    end = time.time()\n",
    "    \n",
    "    ch_score = metrics.silhouette_score(event,mb_kmeans.predict(event))\n",
    "    print(\"ch_score: {}, time elaps:{}\".format(ch_score, int(end-start)))\n",
    "    return ch_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 3\n",
      "ch_score: 0.4997981087466686, time elaps:0\n",
      "K-means begin with clusters: 5\n",
      "ch_score: 0.5303763380573656, time elaps:0\n",
      "K-means begin with clusters: 10\n",
      "ch_score: 0.45983385053182274, time elaps:0\n",
      "K-means begin with clusters: 20\n",
      "ch_score: 0.43250229485931446, time elaps:0\n",
      "K-means begin with clusters: 30\n",
      "ch_score: 0.40163380433035273, time elaps:0\n",
      "K-means begin with clusters: 40\n",
      "ch_score: 0.3346531388401308, time elaps:0\n",
      "K-means begin with clusters: 50\n",
      "ch_score: 0.3429220104439198, time elaps:0\n"
     ]
    }
   ],
   "source": [
    "k_class = [3,5,10,20,30,40,50]\n",
    "ch_scores = []\n",
    "for K in k_class:\n",
    "    ch = cluster_processing(K, event_pca_data)\n",
    "    ch_scores.append(ch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看出聚类成5个类别时，ch_score值最高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LKSksW5aSQmNj3hFZMaUkhF7AooLlxmzdaiR9S9IC0hXC+IJN/SU9IekvkvYs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\nwczMMk4IZmYGOCGYmVnGCcHMzAD4/39gZ0SDgrHjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21dd395c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制不同PCA维数下模型的性能，找到最佳模型／参数（分数最高）\n",
    "plt.plot(k_class, np.array(ch_scores), 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
